Deep Attentional Guided Image Filtering

TL;DR

This paper introduces a deep attentional guided image filtering framework that adaptively combines guidance and target images using learned kernels and multi-scale processing, improving results in various vision tasks.

Contribution

It proposes an innovative attentional kernel learning module and multi-scale filtering strategy to better integrate guidance and target images, reducing artifacts and enhancing filtering performance.

Findings

Outperforms state-of-the-art methods in guided super-resolution

Effective in cross-modality restoration and texture removal

Improves semantic segmentation results

Abstract

Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods construct filter kernels from the guidance itself without considering the mutual dependency between the guidance and the target. However, since there typically exist significantly different edges in the two images, simply transferring all structural information of the guidance to the target would result in various artifacts. To cope with this problem, we propose an effective framework named deep attentional guided image filtering, the filtering process of which can fully integrate the complementary information contained in both images. Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images. Meanwhile, we propose a multi-scale guided image filtering module to progressively generate the filtering result with the constructed kernels in a coarse-to-fine manner. Correspondingly, a multi-scale fusion strategy is introduced to reuse the intermediate results in the coarse-to-fine process. Extensive experiments show that the proposed framework compares favorably with the state-of-the-art methods in a wide range of guided image filtering applications, such as guided super-resolution, cross-modality restoration, texture removal, and semantic segmentation.